AI & ML
Sensing-Based Communication: A Non-Orthogonal Passive Reflection Framework for ISAC
arXiv cs.SY0 views
arXiv:2611.08809v1 Announce Type: cross Abstract: While integrating uplink without disrupting the primary downlink remains a critical challenge for existing reflective communication systems, this paper proposes a non-orthogonal passive integrated sensing and communication (ISAC) mechanism termed Sensing-based Communication. Originating from and validated within an optical retro-reflective system, this mechanism possesses an underlying wave-agnostic architecture with the potential to be generalized across other physical modalities. It leverages incident downlink signals for parasitic modulation via passive tags. By relying solely on fundamental on-off keying (OOK) reflection, the tag unifies the reflective sensing channel and the uplink communication channel, and simultaneously achieves both sensing and duplex communication. Consequently, our approach introduces uplink capability without sacrificing the downlink data rate or consuming additional spectrum resources. At the receiver, we design a joint decoding architecture combining cross-correlation computation and successive interference cancelation (SIC) to fully exploit the degrees of freedom in broadband signals for multi-user interference suppression. We derive closed-form expressions for the interference and bit error rate (BER) assuming zero-mean Gaussian signals. Using an experimental testbed, we validate the pivotal theoretical finding, the scaling relationship of SINR with signal bandwidth and cross-correlation time--where linear regression yields a strong goodness-of-fit with $R^2 = 0.9688$. Furthermore, evaluations under noise-like orthogonal frequency division multiplexing (OFDM) waveforms exhibit high consistency between theoretical derivations and simulation data ($R^2 = 0.9372$, mean absolute percentage error, $\text{MAPE} = 14\%$), confirming the model's accuracy and waveform generalizability.
Source:https://arxiv.org/abs/2611.08809PreviousFishSense Lite: A Camera-Based, Single Laser Hardware Framework with Software System for In Situ Fish Length MeasurementNextAn Ultra-Low-Noise Tunable TIA in 45nm Silicon Photonics for Electro-Optical Ultrasound Data Acquisition
Related posts
Do Top-Ranked Local Businesses Get Recommended by ChatGPT? The November 9, 2026 Re-Check: 0 of 3
0dev.to AI
How to Buy ChatGPT Plus Cheap Without Getting Burned — Top-Ups, Pre-Made Accounts, and CDKs Explained
0dev.to AI

Implementing Capacity Providers in ECS on EC2 for Fully Automated Batch Processing Scaling
0dev.to AWS

Self-heal broken locators with a 2B model on your own GPU
0dev.to Python

AWS Config Costs Were Too High—I Cut Them in Half With One Configuration Change
0dev.to DevOps

Nominal, which builds software to make hardware manufacturing more efficient, raised $240M at a $3B valuation, up from $1B after raising $80M in March (Paayal Zaveri/Bloomberg)
0Techmeme